Lab 15

library(tidyverse)

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library(knitr)
library(ggtree)

## ggtree v3.10.1 For help: https://yulab-smu.top/treedata-book/
## 
## If you use the ggtree package suite in published research, please cite
## the appropriate paper(s):
## 
## Guangchuang Yu, David Smith, Huachen Zhu, Yi Guan, Tommy Tsan-Yuk Lam.
## ggtree: an R package for visualization and annotation of phylogenetic
## trees with their covariates and other associated data. Methods in
## Ecology and Evolution. 2017, 8(1):28-36. doi:10.1111/2041-210X.12628
## 
## Guangchuang Yu.  Data Integration, Manipulation and Visualization of
## Phylogenetic Trees (1st edition). Chapman and Hall/CRC. 2022,
## doi:10.1201/9781003279242
## 
## G Yu. Data Integration, Manipulation and Visualization of Phylogenetic
## Trees (1st ed.). Chapman and Hall/CRC. 2022. ISBN: 9781032233574
## 
## Attaching package: 'ggtree'
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##     expand

library(ggimage)
library(rphylopic)

## You are using rphylopic v.1.4.0. Please remember to credit PhyloPic contributors (hint: `get_attribution()`) and cite rphylopic in your work (hint: `citation("rphylopic")`).
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## Attaching package: 'rphylopic'
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library(treeio)

## treeio v1.26.0 For help: https://yulab-smu.top/treedata-book/
## 
## If you use the ggtree package suite in published research, please cite
## the appropriate paper(s):
## 
## LG Wang, TTY Lam, S Xu, Z Dai, L Zhou, T Feng, P Guo, CW Dunn, BR
## Jones, T Bradley, H Zhu, Y Guan, Y Jiang, G Yu. treeio: an R package
## for phylogenetic tree input and output with richly annotated and
## associated data. Molecular Biology and Evolution. 2020, 37(2):599-603.
## doi: 10.1093/molbev/msz240
## 
## S Xu, Z Dai, P Guo, X Fu, S Liu, L Zhou, W Tang, T Feng, M Chen, L
## Zhan, T Wu, E Hu, Y Jiang, X Bo, G Yu. ggtreeExtra: Compact
## visualization of richly annotated phylogenetic data. Molecular Biology
## and Evolution. 2021, 38(9):4039-4042. doi: 10.1093/molbev/msab166
## 
## Guangchuang Yu, David Smith, Huachen Zhu, Yi Guan, Tommy Tsan-Yuk Lam.
## ggtree: an R package for visualization and annotation of phylogenetic
## trees with their covariates and other associated data. Methods in
## Ecology and Evolution. 2017, 8(1):28-36. doi:10.1111/2041-210X.12628

library(tidytree)

## If you use the ggtree package suite in published research, please cite
## the appropriate paper(s):
## 
## LG Wang, TTY Lam, S Xu, Z Dai, L Zhou, T Feng, P Guo, CW Dunn, BR
## Jones, T Bradley, H Zhu, Y Guan, Y Jiang, G Yu. treeio: an R package
## for phylogenetic tree input and output with richly annotated and
## associated data. Molecular Biology and Evolution. 2020, 37(2):599-603.
## doi: 10.1093/molbev/msz240
## 
## Guangchuang Yu. Using ggtree to visualize data on tree-like structures.
## Current Protocols in Bioinformatics. 2020, 69:e96. doi:10.1002/cpbi.96
## 
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library(ape)

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library(TreeTools)

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library(phytools)

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##     read.newick

NEON_MAGs <- read_csv("data/NEON/GOLD_Study_ID_Gs0161344_NEON_2024_4_21.csv") %>% 
  # remove columns that are not needed for data analysis
  select(-c(`GOLD Study ID`, `Bin Methods`, `Created By`, `Date Added`, `Bin Lineage`)) %>% 
  # create a new column with the Assembly Type
  mutate("Assembly Type" = case_when(`Genome Name` == "NEON combined assembly" ~ `Genome Name`,
                            TRUE ~ "Individual")) %>% 
  mutate_at("Assembly Type", str_replace, "NEON combined assembly", "Combined") %>% 
  mutate_at("GTDB-Tk Taxonomy Lineage", str_replace, "d__", "") %>%  
  mutate_at("GTDB-Tk Taxonomy Lineage", str_replace, "p__", "") %>% 
  mutate_at("GTDB-Tk Taxonomy Lineage", str_replace, "c__", "") %>% 
  mutate_at("GTDB-Tk Taxonomy Lineage", str_replace, "o__", "") %>% 
  mutate_at("GTDB-Tk Taxonomy Lineage", str_replace, "f__", "") %>% 
  mutate_at("GTDB-Tk Taxonomy Lineage", str_replace, "g__", "") %>% 
  mutate_at("GTDB-Tk Taxonomy Lineage", str_replace, "s__", "") %>%
  separate(`GTDB-Tk Taxonomy Lineage`, c("Domain", "Phylum", "Class", "Order", "Family", "Genus", "Species"), ";", remove = FALSE) %>% 
  mutate_at("Domain", na_if,"") %>% 
  mutate_at("Phylum", na_if,"") %>% 
  mutate_at("Class", na_if,"") %>% 
  mutate_at("Order", na_if,"") %>% 
  mutate_at("Family", na_if,"") %>% 
  mutate_at("Genus", na_if,"") %>% 
  mutate_at("Species", na_if,"") %>% 
  
  # Get rid of the the common string "Soil microbial communities from "
  mutate_at("Genome Name", str_replace, "Terrestrial soil microbial communities from ", "") %>% 
  # Use the first `-` to split the column in two
  separate(`Genome Name`, c("Site","Sample Name"), " - ") %>% 
  # Get rid of the the common string "S-comp-1"
  mutate_at("Sample Name", str_replace, "-comp-1", "") %>%
  # separate the Sample Name into Site ID and plot info
  separate(`Sample Name`, c("Site ID","subplot.layer.date"), "_", remove = FALSE,) %>% 
  # separate the plot info into 3 columns
  separate(`subplot.layer.date`, c("Subplot", "Layer", "Date"), "-") 

## Rows: 1754 Columns: 19
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr   (8): Bin ID, Genome Name, Bin Quality, Bin Lineage, GTDB-Tk Taxonomy L...
## dbl  (10): IMG Genome ID, Bin Completeness, Bin Contamination, Total Number ...
## date  (1): Date Added
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

## Warning: Expected 2 pieces. Missing pieces filled with `NA` in 624 rows [1131, 1132,
## 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145,
## 1146, 1147, 1148, 1149, 1150, ...].

NEON_metagenomes <- read_tsv("data/NEON/exported_img_data_Gs0161344_NEON.tsv") %>% 
  select(-c(`Domain`, `Sequencing Status`, `Sequencing Center`)) %>% 
  rename(`Genome Name` = `Genome Name / Sample Name`) %>% 
  filter(str_detect(`Genome Name`, 're-annotation', negate = T)) %>% 
  filter(str_detect(`Genome Name`, 'WREF plot', negate = T)) 

## Rows: 176 Columns: 46
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (18): Domain, Sequencing Status, Study Name, Genome Name / Sample Name, ...
## dbl (16): taxon_oid, IMG Genome ID, Depth In Meters, Elevation In Meters, Ge...
## lgl (12): Altitude In Meters, Chlorophyll Concentration, Longhurst Code, Lon...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

NEON_metagenomes <- NEON_metagenomes %>% 
  # Get rid of the the common string "Soil microbial communities from "
  mutate_at("Genome Name", str_replace, "Terrestrial soil microbial communities from ", "") %>% 
  # Use the first `-` to split the column in two
  separate(`Genome Name`, c("Site","Sample Name"), " - ") %>% 
  # Get rid of the the common string "-comp-1"
  mutate_at("Sample Name", str_replace, "-comp-1", "") %>%
  # separate the Sample Name into Site ID and plot info
  separate(`Sample Name`, c("Site ID","subplot.layer.date"), "_", remove = FALSE,) %>% 
  # separate the plot info into 3 columns
  separate(`subplot.layer.date`, c("Subplot", "Layer", "Date"), "-") 

## Warning: Expected 2 pieces. Missing pieces filled with `NA` in 1 rows [53].

NEON_chemistry <- read_tsv("data/NEON/neon_plot_soilChem1_metadata.tsv") %>% 
  # remove -COMP from genomicsSampleID
  mutate_at("genomicsSampleID", str_replace, "-COMP", "") 

## Rows: 87 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr   (5): genomicsSampleID, siteID, plotID, nlcdClass, horizon
## dbl  (11): decimalLatitude, decimalLongitude, elevation, soilTemp, d15N, org...
## date  (1): collectionDate
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

NEON_MAGs_metagenomes_chemistry <- NEON_MAGs %>% 
  left_join(NEON_metagenomes, by = "Sample Name") %>% 
  left_join(NEON_chemistry, by = c("Sample Name" = "genomicsSampleID"))

tree_arc <- read.tree("data/NEON/gtdbtk.ar53.decorated.tree")
tree_bac <- read.tree("data/NEON/gtdbtk.bac120.decorated.tree")

# Make a vector with the internal node lables
node_vector_bac = c(tree_bac$tip.label,tree_bac$node.label)

# Search for your Phylum or Class
grep("Acidobacteriota", node_vector_bac, value = TRUE)

## [1] "'1.0:p__Acidobacteriota'"

match(grep("Acidobacteriota", node_vector_bac, value = TRUE), node_vector_bac)

## [1] 2673

tree_bac_node_Acidobacteriota <- Preorder(tree_bac)
tree_Acidobacteriota <- Subtree(tree_bac_node_Acidobacteriota, 2673)

ggtree(tree_Acidobacteriota) +
  geom_tiplab(size=3) +
  xlim(0,28)

ggtree(tree_Acidobacteriota, layout="circular") + 
  geom_tiplab(aes(angle=angle))+
    theme_tree() +
    xlim(0,28)